1. Technical Field
The present disclosure relates to an imaging system, an imaging apparatus, and an imaging method using a compressed sensing technology.
2. Description of the Related Art
In imaging systems that process video pictures having high resolution and a high frame rate, a large amount of data are read out from an image sensing device and an imaging module and, thus, a large amount of data are transferred from the image sensing device and the imaging module. As a result, the transfer bandwidth needs to be increased. Thus, the transfer frequency needs to be increased, and parallel data transfer needs to be employed. However, such techniques cause a problem for the image sensing devices and the imaging modules that need to be compact. For example, the image quality decreases due to an increase in noise, and the manufacturing cost increases due to an increase in a circuit scale. Accordingly, reduction of the transfer data volume is expected.
To reduce the transfer data volume, a technology known as compressed sensing is effective. The compressed sensing compresses the data volume by performing addition when signals are sensed (during encoding) and decodes the signals by performing a reconstruction process on the compressed data. Such a sensing method is referred to as “addition sampling”, and image capturing using an image sensor based on the sampling method is referred to as “multiple sampling image capturing”. If multiple sampling image capturing is performed, some amount of image information is lost and, thus, the quality of a reconstructed image significantly deteriorates. To address such an issue, the compressed sensing technology uses the sparsity of an image as previous knowledge. The term “image sparsity” refers to knowledge that when an image is projected into a wavelet space or a discrete cosine (DCT) space, a large number of coefficient values are substantially zero. Compressed sensing uses L0-norm minimization or L1-norm minimization, which is an image reconstruction method using the image sparsity. In this manner, even after the data volume is reduced, a reconstructed image having a quality that is substantially the same as that of an uncompressed image can be obtained.
A method for reconstructing an image generated from data obtained through multiple sampling image capturing by extracting a repetitive structure of an image without using prior learning is described in J. Zhang, D. Zhao, C. Zhao, R. Xiong, S. Ma, and W. Gao, “Compressed Sensing Recovery via Collaborative Sparsity”, Proc. of Data Compression Conference, pp. 287-296, 2012 (hereinafter referred to as “Non-Patent Literature 1”). This method is based on prior knowledge called local similarity. The local similarity indicates that in a natural image, a region of interest has a small region similar thereto in the vicinity thereof.
Imaging apparatuses using the compressed sensing technique compress data by using a simple addition process before an analog-to-digital conversion process is performed by an analog-to-digital converter (hereinafter simply referred to as “ADC”) in an imaging device. Thus, the drive frequency of ADC can be decreased. In this manner, low power consumption, a high S/N ratio, and reduction of transfer bandwidth can be realized.
For example, a solid-state image sensing device that uses the compressed sensing technique is described in Y. Oike and A. E. Gamal, “A 256×256 CMOS Image Sensor with ΔΣ-Based Single-Shot Compressed Sensing”, IEEE International Solid-State Circuits Conference (ISSCC) Dig. of Tech. Papers, pp. 386-387, 2012. The solid-state image sensing device includes a block row selection circuit, a row selection circuit, a column block selection circuit, a multiplexer, a pseudo random pattern generating circuit, and a column parallel ΔΣ-ADC. By using such a configuration, a solid-state image sensing device capable of performing multiple sampling image capturing is achieved.